Super-long span bridges demand high design requirements and involve many difficulties when constructed, which is an important indicator to reflect the bridge technical level of a country. Over the past three decades, a large percentage of the new long-span bridges around the world were built in China, and thus, abundant technological innovations and experience have been accumulated during the design and construction. This paper aims to review and summarize the design and construction practices of the superstructure, the substructure, and the steel deck paving of the long-span bridges during the past decades as well as the current operation status of the existing long-span bridges in China. A future perspective was given on the developing trend of high-speed railway bridge, bridge over deep-sea, health monitoring and maintenance, intellectualization, standard system, and information technology, which is expected to guide the development direction for the construction of future super long-span bridges and promote China to become a strong bridge construction country.

The quest for high-performance construction materials is led by the development and application of new reinforcement materials for cement composites. Concrete reinforcement with fibers has a long history. Nowadays, many new fibers associated with high performance and possessing eco-environmental characteristics, such as basalt fibers and plant fibers, have received much attention from researchers. In addition, nanomaterials are considered as a core material in the modification of cement composites, specifically in the enhancement of the strength and durability of composites. This paper provides an overview of the recent research progress on cement composites reinforced with fibers and nanomaterials. The influences of fibers and nanomaterials on the fresh and hardened properties of cement composites are summarized. Moreover, future trends in the application of these fibers or of nanomaterial-reinforced cement composites are proposed.

Research of reliability of engineering structures has experienced a developing history for more than 90 years. However, the problem of how to resolve the global reliability of structural systems still remains open, especially the problem of the combinatorial explosion and the challenge of correlation between failure modes. Benefiting from the research of probability density evolution theory in recent years, the physics-based system reliability researches open a new way for bypassing this dilemma. The present paper introduces the theoretical foundation of probability density evolution method in view of a broad background, whereby a probability density evolution equation for probability dissipative system is deduced. In conjunction of physical equations and structural failure criteria, a general engineering reliability analysis frame is then presented. For illustrative purposes, several cases are studied which prove the value of the proposed engineering reliability analysis method.

Urban tunnels crossing faults are always at the risk of severe damages. In this paper, the effects of a reverse and a normal fault movement on a transversely crossing shallow shotcreted tunnel are investigated by 3D finite difference analysis. After verifying the accuracy of the numerical simulation predictions with the centrifuge physical model results, a parametric study is then conducted. That is, the effects of various parameters such as the sprayed concrete thickness, the geo-mechanical properties of soil, the tunnel depth, and the fault plane dip angle are studied on the displacements of the ground surface and the tunnel structure, and on the plastic strains of the soil mass around tunnel. The results of each case of reverse and normal faulting are independently discussed and then compared with each other. It is obtained that deeper tunnels show greater displacements for both types of faulting.

A long-span concrete-filled steel tubular (CFST) arch bridge suffers severe vehicle-induced dynamic responses during its service life. However, few quantitative studies have been reported on the typical diseases suffered by such bridges and their effects on vehicle-induced dynamic response. Thus, a series of field tests and theoretical analyses were conducted to study the effects of typical diseases on the vehicle-induced dynamic response of a typical CFST arch bridge. The results show that a support void results in a height difference between both sides of the expansion joint, thus increasing the effect of vehicle impact on the main girder and suspenders. The impact factor of the displacement response of the main girder exceeds the design value. The variation of the suspender force is significant, and the diseases are found to have a greater effect on a shorter suspender. The theoretical analysis results also show that the support void causes an obvious longitudinal displacement of the main girder that is almost as large as the vertical displacement. The support void can also cause significant changes in the vehicle-induced acceleration response, particularly when the supports and steel box girder continue to collide with each other under the vehicle load.

Concrete encased with trapezoidally corrugated web profiled cold-formed steel beams are used worldwide to improve resistance toward fire and corrosion, higher load carrying capacity as well as significant increase in the bending stiffness by encasing concrete on the beam portion. The present work gives a detailed description on the experimental, analytical and numerical investigation on the flexural behavior of concrete encased trapezoidally corrugated web profiled cold-formed steel beams which were simply supported at both ends and subjected to two point symmetric loading. The flexural behavior of such structure has been experimentally tested to failure under pure bending. To find the effect of concrete encasement in the web, 12 experiments were conducted by two different series. Beams having three different web corrugation angles of 0°, 30°, and 45° with two different web depth-thickness (d_w/t_w) ratios of 60 and 80 were tested. Experimental results such as load-deflection relationship, ultimate capacity, load-strain relationship, moment-curvature curves, ductility and failure mode indices of the specimens are presented. From the static bending tests the concrete encased trapezoidally corrugated web beam showed improved moment carrying capacity, ductility behavior and the resistance to transverse deflections in comparison to concrete encased with plain web beam. Especially for the beams with concrete encased 30° trapezoidally corrugated web having (d_w/t_w) ratio 60 and 80, the loading capacity was improved about 54% and 67.3% and the ductility also increased about 1.6 and 3.6 times, when compared to concrete encased beams with plain web. This research should contribute to the future engineering applications on seismic resistant structures and efficient usage of concrete encased with cold-formed steel beams by exhibiting its super elasto-plastic property. The analytical and numerical results showed good agreement with the experimental results at yield load, which indicates that the proposed analytical equations can be applied in predicting flexural strength accurately for such concrete encased trapezoidally corrugated web profiled cold-formed steel beams.

In this study, a total of 177 flexural experimental tests of corroded reinforced concrete (CRC) beams were collected from the published literature. The database of flexural capacity of CRC beam was established by using unified and standardized experimental data. Through this database, the effects of various parameters on the flexural capacity of CRC beams were discussed, including beam width, the effective height of beam section, ratio of strength between longitudinal reinforcement and concrete, concrete compressive strength, and longitudinal reinforcement corrosion ratio. The results indicate that the corrosion of longitudinal reinforcement has the greatest effect on the residual flexural capacity of CRC beams, while other parameters have much less effect. In addition, six available empirical models for calculating the residual flexural strength of CRC beams were also collected and compared with each other based on the established database. It indicates that though five of six existing empirical models underestimate the flexural capacity of CRC beams, there is one model overestimating the flexural capacity. Finally, a newly developed empirical model is proposed to provide accurate and effective predictions in a large range of corrosion ratio for safety assessment of flexural failure of CRC beams confirmed by the comparisons.

A real-time vehicle monitoring is crucial for effective bridge maintenance and traffic management because overloaded vehicles can cause damage to bridges, and in some extreme cases, it will directly lead to a bridge failure. Bridge weigh-in-motion (BWIM) system as a high performance and cost-effective technology has been extensively used to monitor vehicle speed and weight on highways. However, the dynamic effect and data noise may have an adverse impact on the bridge responses during and immediately following the vehicles pass the bridge. The fast Fourier transform (FFT) method, which can significantly purify the collected structural responses (dynamic strains) received from sensors or transducers, was used in axle counting, detection, and axle weighing technology in this study. To further improve the accuracy of the BWIM system, the field-calibrated influence lines (ILs) of a continuous multi-girder bridge were regarded as a reference to identify the vehicle weight based on the modified Moses algorithm and the least squares method. In situ experimental results indicated that the signals treated with FFT filter were far better than the original ones, the efficiency and the accuracy of axle detection were significantly improved by introducing the FFT method to the BWIM system. Moreover, the lateral load distribution effect on bridges should be considered by using the calculated average ILs of the specific lane individually for vehicle weight calculation of this lane.

Superabsorbent Polymer (SAP) has emerged as a topic of considerable interest in recent years. The present study systematically and quantitively investigated the effect of SAP on hydration, autogenous shrinkage, mechanical properties, and microstructure of cement mortars. Influences of SAP on hydration heat and autogenous shrinkage were studied by utilizing TAM AIR technology and a non-contact autogenous shrinkage test method. Scanning Electron Microscope (SEM) was employed to assess the microstructure evolution. Although SAP decreased the peak rate of hydration heat and retarded the hydration, it significantly increased the cumulative heat, indicating SAP helps promote the hydration. Hydration promotion caused by SAP mainly occurred in the deceleration period and attenuation period. SAP can significantly mitigate the autogenous shrinkage when the content ranged from 0 to 0.5%. Microstructure characteristics showed that pores and gaps were introduced when SAP was added. The microstructure difference caused by SAP contributed to the inferior mechanical behaviors of cement mortars treated by SAP.

Homogenization methods can be used to predict the effective macroscopic properties of materials that are heterogenous at micro- or fine-scale. Among existing methods for homogenization, computational homogenization is widely used in multiscale analyses of structures and materials. Conventional computational homogenization suffers from long computing times, which substantially limits its application in analyzing engineering problems. The neural networks can be used to construct fully decoupled approaches in nonlinear multiscale methods by mapping macroscopic loading and microscopic response. Computational homogenization methods for nonlinear material and implementation of offline multiscale computation are studied to generate data set. This article intends to model the multiscale constitution using feedforward neural network (FNN) and recurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict the materials behavior along unknown paths. Applications to two-dimensional multiscale analysis are tested and discussed in detail.

This study investigates the technique of variational calculus applied to estimate the slope stability considering the mechanism of planar failure. The critical plane failure surface should be determined because it theoretically indicates the most unfavorable plane to be considered when stabilizing a slope to rectify the instability generated by several statistically possible planes. This generates integrals that can be solved by numerical methods, such as the Newton Cotes and the finite differences methods. Additionally, a system of nonlinear equations is obtained and solved. The surface of the critical planar failure is determined by applying the condition of transversality in mobile boundaries, for which various examples are provided. The number of slices is varied in one of the examples, while the surface of the critical planar failure is determined in the others. Results are compared using analytical methods through axis rotations. All the results obtained by considering normal stress, safety factors, and critical planar failure are nearly the same; however, in this research, a study is carried out for “n” number of slices using programming methods. Sub-routines are important because they can be applied in slopes with different geometry, surcharge, interstitial pressure, and pseudo-static load.

This paper presents a developed meta-heuristic algorithm to optimize the shear walls of tall reinforced concrete buildings. These types of walls are considered as lateral resistant elements. In this paper, Quantum Charged System Search (QCSS) algorithm is presented as a new optimization method and used to improve the convergence capability of the original Charged System Search. The cost of tall building is taken as the objective function. Since the design of the lateral system plays a major role in the performance of the tall buildings, this paper proposes a unique computational technique that, unlike available works, focuses on structural efficiency or architectural design. This technique considers both structural and architectural requirements such as minimum structural costs, torsional effects, flexural and shear resistance, lateral deflection, openings and accessibility. The robustness of the new algorithm is demonstrated by comparing the outcomes of the QCSS with those of its standard algorithm.

This paper presents a state-of-the-art review of research on the utilization of fibers (predominantly derived from waste materials) as reinforcement in adobe brick production. Recycling of these wastes provides sustainable construction materials and helps to protect the environment. Specimen preparation and test procedures are outlined. The effects of addition of these wastes on the physical and mechanical properties of adobe bricks as presented in the literature, are investigated. The main results for each additive are presented and discussed. It is concluded that improved adobe brick properties can be expected with the addition of combination of waste additives. The use of waste materials in the construction industry is generally of interest and useful for engineers and designers seeking sustainable solutions in construction. It is also of interest to researchers actively seeking to develop methodical approaches to quantifying, optimising and testing the performance in use of such waste material additives.

The current work experimentally explores and then theoretically examines the lateral vibrations of an unbalanced Jeffcott rotor-system working at several unbalance conditions. To this end, three conditions of eccentric masses are considered by using a Bently Nevada RK-4 rotor kit. Measurements of the steady-state as well as the startup data at rigid and flexible rotor states are captured by conducting a setup that mimics the vibration monitoring industrial practices. The linear governing equation of the considered rotor is extracted by adopting the Lagrange method on the basis of rigid rotor assumptions to theoretically predict the lateral vibrations. The dynamic features of the rotor system such as the linearized bearing induced stiffness are exclusively acquired from startup data. It is demonstrated that, with an error of less than 5%, the proposed two-degrees-of-freedom model can predict the flexural vibrations at rigid condition. While at flexible condition, it fails to accurately predict the dynamic response. In contrast to the other works where nonlinear mathematical models with some complexities are proposed to mathematically model the real systems, the present study illustrates the applicability of employing simple models to predict the dynamic response of a real rotor-system with an acceptable accuracy.

In this study, the performance of an efficient two-stage methodology which is applied in a damage detection system using a surrogate model of the structure has been investigated. In the first stage, in order to locate the damage accurately, the performance of the modal strain energy based index for using different numbers of natural mode shapes has been evaluated using the confusion matrix. In the second stage, to estimate the damage extent, the sensitivity of most used modal properties due to damage, such as natural frequency and flexibility matrix is compared with the mean normalized modal strain energy (MNMSE) of suspected damaged elements. Moreover, a modal property change vector is evaluated using the group method of data handling (GMDH) network as a surrogate model during damage extent estimation by optimization algorithm; in this part of methodology, the performance of the three popular optimization algorithms including particle swarm optimization (PSO), bat algorithm (BA), and colliding bodies optimization (CBO) is examined and in this regard, root mean square deviation (RMSD) based on the modal property change vector has been proposed as an objective function. Furthermore, the effect of noise in the measurement of structural responses by the sensors has also been studied. Finally, in order to achieve the most generalized neural network as a surrogate model, GMDH performance is compared with a properly trained cascade feed-forward neural network (CFNN) with log-sigmoid hidden layer transfer function. The results indicate that the accuracy of damage extent estimation is acceptable in the case of integration of PSO and MNMSE. Moreover, the GMDH model is also more efficient and mimics the behavior of the structure slightly better than CFNN model.

Precast concrete structures have developed rapidly in the last decades due to the advantages of better quality, non-pollution and fast construction with respect to conventional cast-in-place structures. In the present study, a theoretical model and nonlinear 3D model are developed and established to assess the dynamic behavior of precast concrete slabs under blast load. At first, the 3D model is validated by an experiment performed by other researchers. The verified model is adopted to investigate the blast performance of fabricated concrete panels (FCPs) in terms of parameters of the explosive charge, panel thickness, and reinforcement ratio. Finally, a simplified theoretical model of the FCP under blast load is developed to predict the maximum deflection. It is indicated that the theoretical model can precisely predict the maximum displacement of FCP under blast loads. The results show that the failure modes of the panels varied from bending failure to shear failure with the mass of TNT increasing. The thickness of the panel, reinforcement ratio, and explosive charges have significant effects on the anti-blast capacity of the FCPs.

The loading capacity in the axial direction of a bolted thin steel plate was investigated. A refined numerical model of bolt was first constructed and then validated using existing experiment results. Parametrical analysis was performed to reveal the influences of geometric parameters, including the effective depth of the cap nut, the yield strength of the steel plate, the preload of the bolt, and shear force, on the ultimate loading capacity. Then, an analytical method was proposed to predict the ultimate load of the bolted thin steel plate. Results derived using the numerical and analytical methods were compared and the results indicated that the analytical method can accurately predict the pull-through capacity of bolted thin steel plates. The work reported in this paper can provide a simplified calculation method for the loading capacity in the axial direction of a bolt.

Scour depth around bridge piers plays a vital role in the safety and stability of the bridges. The former approaches used in the prediction of scour depth are based on regression models or black box models in which the first one lacks enough accuracy while the later one does not provide a clear mathematical expression to easily employ it for other situations or cases. Therefore, this paper aims to develop new equations using particle swarm optimization as a metaheuristic approach to predict scour depth around bridge piers. To improve the efficiency of the proposed model, individual equations are derived for laboratory and field data. Moreover, sensitivity analysis is conducted to achieve the most effective parameters in the estimation of scour depth for both experimental and filed data sets. Comparing the results of the proposed model with those of existing regression-based equations reveal the superiority of the proposed method in terms of accuracy and uncertainty. Moreover, the ratio of pier width to flow depth and ratio of d50 (mean particle diameter) to flow depth for the laboratory and field data were recognized as the most effective parameters, respectively. The derived equations can be used as a suitable proxy to estimate scour depth in both experimental and prototype scales.

Landslide is a common geological hazard in reservoir areas and may cause great damage to local residents’ life and property. It is widely accepted that rainfall and periodic variation of water level are the two main factors triggering reservoir landslides. In this study, the Bazimen landslide located in the Three Gorges Reservoir (TGR) was back-analyzed as a case study. Based on the statistical features of the last 3-year monitored data and field instrumentations, the landslide susceptibility in an annual cycle and four representative periods was investigated via the deterministic and probabilistic analysis, respectively. The results indicate that the fluctuation of the reservoir water level plays a pivotal role in inducing slope failures, for the minimum stability coefficient occurs at the rapid decline period of water level. The probabilistic analysis results reveal that the initial sliding surface is the most important area influencing the occurrence of landslide, compared with other parts in the landslide. The seepage calculations from probabilistic analysis imply that rainfall is a relatively inferior factor affecting slope stability. This study aims to provide preliminary guidance on risk management and early warning in the TGR area.

This article proposes a novel methodology that uses mathematical and numerical models of a structure to build a data set and determine crucial nodes that possess the highest sensitivity. Regression surfaces between the structural parameters and structural output features, represented by the natural frequencies of the structure and local transmissibility, are built using the numerical data set. A description of a possible experimental application is provided, where sensors are mounted at crucial nodes, and the natural frequencies and local transmissibility at each natural frequency are determined from the power spectral density and the power spectral density ratios of the sensor responses, respectively. An inverse iterative process is then applied to identify the structural parameters by matching the experimental features with the available parameters in the myriad numerical data set. Three examples are presented to demonstrate the feasibility and efficacy of the proposed methodology. The results reveal that the method was able to accurately identify the boundary coefficients and physical parameters of the Euler-Bernoulli beam as well as a highway bridge model with elastic foundations using only two measurement points. It is expected that the proposed method will have practical applications in the identification and analysis of restored structural systems with unknown parameters and boundary coefficients.

The tensile behavior of hybrid fiber reinforced concrete (HFRC) is important to the design of HFRC and HFRC structure. This study used an artificial neural network (ANN) model to describe the tensile behavior of HFRC. This ANN model can describe well the tensile stress-strain curve of HFRC with the consideration of 23 features of HFRC. In the model, three methods to process output features (no-processed, mid-processed, and processed) are discussed and the mid-processed method is recommended to achieve a better reproduction of the experimental data. This means the strain should be normalized while the stress doesn’t need normalization. To prepare the database of the model, both many direct tensile test results and the relevant literature data are collected. Moreover, a traditional equation-based model is also established and compared with the ANN model. The results show that the ANN model has a better prediction than the equation-based model in terms of the tensile stress-strain curve, tensile strength, and strain corresponding to tensile strength of HFRC. Finally, the sensitivity analysis of the ANN model is also performed to analyze the contribution of each input feature to the tensile strength and strain corresponding to tensile strength. The mechanical properties of plain concrete make the main contribution to the tensile strength and strain corresponding to tensile strength, while steel fibers tend to make more contributions to these two items than PVA fibers.

Some of the current concrete damage plasticity models in the literature employ a single damage variable for both the tension and compression regimes, while a few more advanced models employ two damage variables. Models with a single variable have an inherent difficulty in accounting for the damage accrued due to tensile and compressive actions in appropriately different manners, and their mutual dependencies. In the current models that adopt two damage variables, the independence of these damage variables during cyclic loading results in the failure to capture the effects of tensile damage on the compressive behavior of concrete and vice-versa. This study presents a cyclic model established by extending an existing monotonic constitutive model. The model describes the cyclic behavior of concrete under multiaxial loading conditions and considers the influence of tensile/compressive damage on the compressive/tensile response. The proposed model, dubbed the enhanced concrete damage plasticity model (ECDPM), is an extension of an existing model that combines the theories of classical plasticity and continuum damage mechanics. Unlike most prior studies on models in the same category, the performance of the proposed ECDPM is evaluated using experimental data on concrete specimens at the material level obtained under cyclic multiaxial loading conditions including uniaxial tension and confined compression. The performance of the model is observed to be satisfactory. Furthermore, the superiority of ECDPM over three previously proposed constitutive models is demonstrated through comparisons with the results of a uniaxial tension-compression test and a virtual test.

We propose a new algorithm, named Asymmetric Genetic Algorithm (AGA), for solving optimization problems of steel frames. The AGA consists of a developed penalty function, which helps to find the best generation of the population. The objective function is to minimize the weight of the whole steel structure under the constraint of ultimate loads defined for structural steel buildings by the American Institute of Steel Construction (AISC). Design variables are the cross-sectional areas of elements (beams and columns) that are selected from the sets of side-flange shape steel sections provided by the AISC. The finite element method (FEM) is utilized for analyzing the behavior of steel frames. A 15-storey three-bay steel planar frame is optimized by AGA in this study, which was previously optimized by algorithms such as Particle Swarm Optimization (PSO), Particle Swarm Optimizer with Passive Congregation (PSOPC), Particle Swarm Ant Colony Optimization (HPSACO), Imperialist Competitive Algorithm (ICA), and Charged System Search (CSS). The results of AGA such as total weight of the structure and number of analyses are compared with the results of these algorithms. AGA performs better in comparison to these algorithms with respect to total weight and number of analyses. In addition, five numerical examples are optimized by AGA, Genetic Algorithm (GA), and optimization modules of SAP2000, and the results of them are compared. The results show that AGA can decrease the time of analyses, the number of analyses, and the total weight of the structure. AGA decreases the total weight of regular and irregular steel frame about 11.1% and 26.4% in comparing with the optimized results of SAP2000, respectively.

The unprecedented liquefaction-related land damage during earthquakes has highlighted the need to develop a model that better interprets the liquefaction land damage vulnerability (LLDV) when determining whether liquefaction is likely to cause damage at the ground’s surface. This paper presents the development of a novel comprehensive framework based on select case history records of cone penetration tests using a Bayesian belief network (BBN) methodology to assess seismic soil liquefaction and liquefaction land damage potentials in one model. The BBN-based LLDV model is developed by integrating multi-related factors of seismic soil liquefaction and its induced hazards using a machine learning (ML) algorithm-K2 and domain knowledge (DK) data fusion methodology. Compared with the C4.5 decision tree-J48 model, naive Bayesian (NB) classifier, and BBN-K2 ML prediction methods in terms of overall accuracy and the Cohen’s kappa coefficient, the proposed BBN K2 and DK model has a better performance and provides a substitutive novel LLDV framework for characterizing the vulnerability of land to liquefaction-induced damage. The proposed model not only predicts quantitatively the seismic soil liquefaction potential and its ground damage potential probability but can also identify the main reasons and fault-finding state combinations, and the results are likely to assist in decisions on seismic risk mitigation measures for sustainable development. The proposed model is simple to perform in practice and provides a step toward a more sophisticated liquefaction risk assessment modeling. This study also interprets the BBN model sensitivity analysis and most probable explanation of seismic soil liquefied sites based on an engineering point of view.

The physio-chemical changes in concrete mixes due to different coarse aggregate (natural coarse aggregate and recycled coarse aggregate (RCA)) and mix design methods (conventional method and Particle Packing Method (PPM)) are studied using thermogravimetric analysis of the hydrated cement paste. A method is proposed to estimate the degree of hydration (\alpha) from chemically bound water (W_{\mathrm{B}}). The PPM mix designed concrete mixes exhibit lower \alpha. Recycled aggregate concrete (RAC) mixes exhibit higher and \alpha after 7 d of curing, contrary to that after 28 and 90 d. The chemically bound water at infinite time (W_{\mathrm{B}\infty }) of RAC mixes are lower than the respective conventional concrete mixes. The lower W_{\mathrm{B}\infty }, Ca(OH)₂ bound water, free Ca(OH)₂ content and FT-IR analysis substantiate the use of pozzolanic cement in the parent concrete of RCA. The compressive strength of concrete and \alpha cannot be correlated for concrete mixes with different aggregate type and mix design method as the present study confirms that the degree of hydration is not the only parameter which governs the macro-mechanical properties of concrete. In this regard, further study on the influence of interfacial transition zone, voids content and aggregate quality on macro-mechanical properties of concrete is needed.

A theoretical solution is aimed to be developed in this research for predicting the failure in internally pressurized composite pressure vessels exposed to low-velocity impact. Both in-plane and out-of-plane failure modes are taken into account simultaneously and thus all components of the stress and strain fields are derived. For this purpose, layer-wise theory is employed in a composite cylinder under internal pressure and low-velocity impact. Obtained stress/strain components are fed into appropriate failure criteria for investigating the occurrence of failure. In case of experiencing any in-plane failure mode, the evolution of damage is modeled using progressive damage modeling in the context of continuum damage mechanics. Namely, mechanical properties of failed ply are degraded and stress analysis is performed on the updated status of the model. In the event of delamination occurrence, the solution is terminated. The obtained results are validated with available experimental observations in open literature. It is observed that the sequence of in-plane failure and delamination varies by increasing the impact energy.

A constrained back propagation neural network (C-BPNN) model for standard penetration test based soil liquefaction assessment with global applicability is developed, incorporating existing knowledge for liquefaction triggering mechanism and empirical relationships. For its development and validation, a comprehensive liquefaction data set is compiled, covering more than 600 liquefaction sites from 36 earthquakes in 10 countries over 50 years with 13 complete information entries. The C-BPNN model design procedure for liquefaction assessment is established by considering appropriate constraints, input data selection, and computation and calibration procedures. Existing empirical relationships for overburden correction and fines content adjustment are shown to be able to improve the prediction success rate of the neural network model, and are thus adopted as constraints for the C-BPNN model. The effectiveness of the C-BPNN method is validated using the liquefaction data set and compared with that of several liquefaction assessment methods currently adopted in engineering practice. The C-BPNN liquefaction model is shown to have improved prediction accuracy and high global adaptability.

Permeability is a major indicator of concrete durability, and depends primarily on the microstructure characteristics of concrete, including its porosity and pore size distribution. In this study, a variety of concrete samples were prepared to investigate their microstructure characteristics via nuclear magnetic resonance (NMR), mercury intrusion porosimetry (MIP), and X-ray computed tomography (X-CT). Furthermore, the chloride diffusion coefficient of concrete was measured to explore its correlation with the microstructure of the concrete samples. Results show that the proportion of pores with diameters<1000 nm obtained by NMR exceeds that obtained by MIP, although the difference in the total porosity determined by both methods is minimal. X-CT measurements obtained a relatively small porosity; however, this likely reflects the distribution of large pores more accurately. A strong correlation is observed between the chloride diffusion coefficient and the porosity or contributive porosity of pores with sizes<1000 nm. Moreover, microstructure parameters measured via NMR reveal a lower correlation coefficient R² versus the chloride diffusion coefficient relative to the parameters determined via MIP, as NMR can measure non-connected as well as connected pores. In addition, when analyzing pores with sizes>50 µm, X-CT obtains the maximal contributive porosity, followed by MIP and NMR.